Field
The disclosed embodiments relate to techniques for processing time-series data. More specifically, the disclosed embodiments relate to techniques for adjusting timestamps of time-series data from remote systems to account for time zone differences based on automatically identified time offsets of the remote systems.
Related Art
Organizations are increasingly relying on cloud-based computing systems to perform large-scale computational tasks. Such cloud-based computing systems are typically operated by hosting companies that maintain a sizable computational infrastructure, often comprising thousands of servers sited in geographically distributed data centers. Customers typically buy or lease computational resources from these hosting companies. The hosting companies in turn provision computational resources according to the customers' requirements and then enable the customers to access these resources.
Organizations further face the challenge of collecting and analyzing data from distributed cloud-computing environments. By performing “big data” analytics on logs, reports, errors, network data, and/or other monitored events in cloud-computing environments, the organizations may glean valuable insights that can be used to guide decisions and/or actions related to the data. For example, business analytics may facilitate the assessment of past performance, guiding of business planning, and/or identification of actions that may improve future performance. Similarly, time-stamped events in a distributed computing environment may be monitored to determine usage patterns of hardware or software resources and detect anomalies in the operation or use of the resources.
However, time-series data may be collected from remote computing environments located in multiple time zones. In addition, some or all of the time-series data may lack time zone information that can be used to normalize timestamps in the time-series data to a time standard such as Coordinated Universal Time (UTC). As a result, an administrator may be required to manually obtain the time zone for a given remote computing environment and create a system configuration that uses the time zone to adjust timestamps from the remote computing environment to conform to UTC or another time standard.
Consequently, collection and analysis of time-series data from remote computing environments may be facilitated by mechanisms for automatically standardizing timestamps from the remote computing environments.